Let's Build Applications
with Homomorphic
Encryption

Open source cryptographic tools that make protecting privacy easy.

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Homomorphic encryption enables applications to run privately by processing data blindly.

Write python code, run it on encrypted data

Zama's Concrete Framework enables data scientists to build models that run on encrypted data, without learning cryptography. Just write Python code and Concrete will convert it to an homomorphic equivalent!


import concrete.numpy as hnp

def add(x, y):
    return x + y

inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1), (3, 2), (6, 1), (1, 7), (4, 5), (5, 4)]
compiler = hnp.NPFHECompiler(add, {"x": "encrypted", "y": "encrypted"})

print(f"Compiling...")
circuit = compiler.compile_on_inputset(inputset)

examples = [(3, 4), (1, 2), (7, 7), (0, 0)]
for example in examples:
    result = circuit.run(*example)
    print(f"Evaluation of {' + '.join(map(str, example))} homomorphically = {result}")

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Use low-level FHE operators to fine-tune execution

Cryptographers looking to manipulate FHE operators directly can do so using Concrete’s low-level library. Built in Rust using a highly modual architecture, it makes extending Concrete safe and easy.

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use concrete::*;

fn main() -> Result<(), CryptoAPIError> {

    // generate a secret key
    let secret_key = LWESecretKey::new(&LWE128_630);

    // the two values to add
    let m1 = 8.2;
    let m2 = 5.6;

    // Encode in [0, 10[ with 8 bits of precision and 1 bit of padding
    let encoder = Encoder::new(0., 10., 8, 1)?;

    // encrypt plaintexts
    let mut c1 = LWE::encode_encrypt(&secret_key, m1, &encoder)?;
    let c2 = LWE::encode_encrypt(&secret_key, m2, &encoder)?;

    // add the two ciphertexts homomorphically, and store in c1
    c1.add_with_padding_inplace(&c2)?;

    // decrypt and decode the result
    let m3 = c1.decrypt_decode(&secret_key)?;

    // print the result and compare to non-FHE addition
    println!("Real: {}, FHE: {}", m1 + m2, m3);

    Ok(())
}

Concrete ML Makes Use Cases Easy

With Concrete ML, we are able to show some very appealing examples of how the tool can be used with models that are already familiar to data scientists.


from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from concrete.ml.sklearn import LogisticRegression

# Create a synthetic dataset
N_EXAMPLE_TOTAL = 100
N_TEST = 20
x, y = make_classification(n_samples=N_EXAMPLE_TOTAL, class_sep=2, n_features=4, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
    x, y, test_size=N_TEST / N_EXAMPLE_TOTAL, random_state=42)
    
# Fix the quantization to 3 bits
model = LogisticRegression(n_bits=3)

# Fit the model
model.fit(X_train, y_train)

# We run prediction on non-encrypted data as a reference
y_pred_clear = model.predict(X_test, execute_in_fhe=False)

# We compile into an FHE model
model.compile(x)

# We then run the inference in FHE
y_pred_fhe = model.predict(X_test, execute_in_fhe=True)
print("In clear  :", y_pred_clear)
print("In FHE    :", y_pred_fhe)
print("Comparison:", (y_pred_fhe == y_pred_clear))

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Write python code, run it on encrypted data

Data scientists looking to run their models on encrypted data can now do so without learning cryptography. Just build your model using Numpy. Concrete will convert it to an optimized FHE executable!

<script>


var tricksWord = document.getElementsByClassName("tricks");
for (var i = 0; i < tricksWord.length; i++) {

var wordWrap = tricksWord.item(i);
wordWrap.innerHTML = wordWrap.innerHTML.replace(/(^|<\/?[^>]+>|\s)([^\s<]+)/g, 
'$1$2');

}

var tricksLetter = document.getElementsByClassName("tricksword");
for (var i = 0; i < tricksLetter.length; i++) {

   var letterWrap = tricksLetter.item(i);
   letterWrap.innerHTML = letterWrap.textContent.replace(/\S/g, "$&");

}
  
</script>

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Homomorphic Encryption Brings Privacy to Web2 and Web3 Applications

Facial Recognition

Facial recognition is now a part of our everyday experience. This however requires someone to have your biometric facial recognition, which in the wrong hands can be used to impersonate you. With FHE, you can authenticate yourself securely, without anybody being able to steal your biometric data.
‍

→ Learn more

Voice Assistants

When your family speaks to a voice assistant, they send personal information to the companies behind them. With FHE, you could send voice queries to AI assistants, who would have no way of knowing what you just asked. It wouldn’t matter that you have a microphone in your house, nobody would be able to listen to what you say!

→ Learn more

Smart Contracts

By design, blockchains are public, meaning all the user data flowing into web3 applications are visible to the entire world. With FHE, we can enable private smart contracts, where the inputs and outputs are encrypted end to end, meaning you can safely build decentralized applications that use sensitive personal
data.

→ Learn more

Let's Build Applications
with Homomorphic
Encryption

Homomorphic encryption enables applications to run privately by processing data blindly.

Write python code, run it on encrypted data

Use low-level FHE operators to fine-tune execution

Concrete ML Makes Use Cases Easy

Write python code, run it on encrypted data

Homomorphic Encryption Brings Privacy to Web2 and Web3 Applications

Facial Recognition

Voice Assistants

Smart Contracts

Developers

Products

Company

Let's Build Applications with HomomorphicEncryption

Homomorphic encryption enables applications to run privately by processing data blindly.

Write python code, run it on encrypted data

Use low-level FHE operators to fine-tune execution

Concrete ML Makes Use Cases Easy

Write python code, run it on encrypted data

Homomorphic Encryption Brings Privacy to Web2 and Web3 Applications

Facial Recognition

Voice Assistants

Smart Contracts

Developers

Products

Company

Let's Build Applications
with Homomorphic
Encryption